What is an example of a weak correlation?
What is an example of a weak correlation?
A weak correlation means that as one variable increases or decreases, there is a lower likelihood of there being a relationship with the second variable. Earthquake magnitude and the depth at which it was measured is therefore weakly correlated, as you can see the scatter plot is nearly flat.
Is 0.2 A weak correlation?
There is no rule for determining what size of correlation is considered strong, moderate or weak. For this kind of data, we generally consider correlations above 0.4 to be relatively strong; correlations between 0.2 and 0.4 are moderate, and those below 0.2 are considered weak.
How do you test if a correlation is statistically significant?
Compare r to the appropriate critical value in the table. If r is not between the positive and negative critical values, then the correlation coefficient is significant. If r is significant, then you may want to use the line for prediction. Suppose you computed r=0.801 using n=10 data points.
What is most likely the correlation coefficient?
0.19 is most likely the correlation coefficient for the set of data shown. Step-by-step explanation: The higher the correlation coefficient, up to 1.0 or -1.0, the better the fit. A positive correlation coefficient means an increasing data set, while a negative correlation coefficient means a decreasing data set.
What R value represents the strongest negative correlation?
-0.7
What is a significant correlation?
To determine whether the correlation between variables is significant, compare the p-value to your significance level. Usually, a significance level (denoted as α or alpha) of 0.05 works well. An α of 0.05 indicates that the risk of concluding that a correlation exists—when, actually, no correlation exists—is 5%.
What is the null hypothesis for correlation?
For a product-moment correlation, the null hypothesis states that the population correlation coefficient is equal to a hypothesized value (usually 0 indicating no linear correlation), against the alternative hypothesis that it is not equal (or less than, or greater than) the hypothesized value.